Cancer Imaging最新文献

{"title":"The sonographic characteristics of unicentric castleman disease - a single-center retrospective study.","authors":"Zihan Liu, Zihan Niu, Yuhan Gao, Mengsu Xiao, Ying Wang, Qingli Zhu, Lu Zhang","doi":"10.1186/s40644-025-00937-2","DOIUrl":"10.1186/s40644-025-00937-2","url":null,"abstract":"<p><strong>Background: </strong>Unicentric Castleman disease (UCD) is a rare group of non-neoplastic lymphoproliferative disorders. This study aims to summarize the specific ultrasonic manifestations of UCD.</p><p><strong>Methods: </strong>This retrospective study included patients who underwent preoperative ultrasound for enlarged lymph nodes and were later diagnosed with UCD between January 2016 and March 2024. Ultrasound features, including lymph node size, cortical characteristics, corticomedullary interface, hyperechoic regions, and Doppler flow signals, were recorded. Pathological types were classified as hyaline vascular (HV), plasma cell (PC), or mixed. The ultrasonic features of each UCD subtype were systematically analyzed.</p><p><strong>Results: </strong>A total of 41 patients were enrolled in the study, comprising 29 with HV-type, 4 with PC-type, and 8 with a mixed type. All patients presented with enlarged lymph nodes (LNs) characterized by a solitary mass, well-defined margins, and increased cortical thickness. Among these, 95.12% (39/41) exhibited an indistinct corticomedullary interface. Additionally, 41.46% (17/41) showed eccentric or asymmetrical cortical thickening, while 58.54% (24/41) demonstrated complete effacement of the fatty hilum. Approximately 24.39% (10/41) of cases exhibited macrocalcification, and 56.10% (23/41) displayed short linear hyperechoic foci within the lymph nodes. Furthermore, patients with HV-type and mixed-type conditions exhibited more abundant blood flow signals compared to those with PC-type (75.86% vs. 25% vs. 87.50%, P = 0.018).</p><p><strong>Conclusions: </strong>Ultrasound characteristics of UCD generally comprise sizable, solitary masses with clearly delineated borders, a thickened cortex, and disappearance of the fatty hilum. Principal imaging indicators encompass microcalcifications and short linear hyper-echoes. Ultrasound represents an effective and non-invasive modality for the early identification and diagnosis of UCD.</p><p><strong>Trial registration: </strong>Retrospectively registered.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"117"},"PeriodicalIF":3.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision medicine in prostate cancer: individualized treatment through radiomics, genomics, and biomarkers.","authors":"Kang Min, Qing Lin, Daoxian Qiu","doi":"10.1186/s40644-025-00938-1","DOIUrl":"10.1186/s40644-025-00938-1","url":null,"abstract":"<p><p>Prostate cancer (PCa) is one of the most common malignancies threatening men's health globally. A comprehensive and integrated approach is essential for its early screening, diagnosis, risk stratification, treatment guidance, and efficacy assessment. Radiomics, leveraging multi-parametric magnetic resonance imaging (mpMRI) and positron emission tomography/computed tomography (PET/CT), has demonstrated significant clinical value in the non-invasive diagnosis, aggressiveness assessment, and prognosis prediction of PCa, with substantial potential when combined with artificial intelligence. In genomics, mutations or deletions in genes such as TMPRSS2-ERG, PTEN, RB1, TP53, and DNA damage repair genes (e.g., BRCA1/2) are closely associated with disease development and progression, holding profound implications for diagnosis, treatment, and prognosis. Concurrently, biomarkers like prostate-specific antigen (PSA), novel urinary markers (e.g., PCA3), and circulating tumor cells (CTCs) are widely utilized in PCa research and management. Integrating these technologies into personalized treatment plans and the broader framework of precision medicine allows for an in-depth exploration of the relationship between specific biomarkers and disease pathogenesis. This review summarizes the current research on radiomics, genomics, and biomarkers in PCa, and discusses their future potential and applications in advancing individualized patient care.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"116"},"PeriodicalIF":3.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A radiomics-based machine learning model and SHAP for predicting spread through air spaces and its prognostic implications in stage I lung adenocarcinoma: a multicenter cohort study.","authors":"Yuhang Wang, Xufeng Liu, Xiaojiang Zhao, Zixiao Wang, Xin Li, Daqiang Sun","doi":"10.1186/s40644-025-00935-4","DOIUrl":"10.1186/s40644-025-00935-4","url":null,"abstract":"<p><strong>Background: </strong>Despite early detection via low-dose computed tomography and complete surgical resection for early-stage lung adenocarcinoma, postoperative recurrence remains high, particularly in patients with tumor spread through air spaces. A reliable preoperative prediction model is urgently needed to adjust the treatment modality.</p><p><strong>Methods: </strong>In this multicenter retrospective study, 609 patients with pathological stage I lung adenocarcinoma from 3 independent centers were enrolled. Regions of interest for the primary tumor and peritumoral areas (extended by three, six, and twelve voxel units) were manually delineated from preoperative CT imaging. Quantitative imaging features were extracted and filtered by correlation analysis and Random forest Ranking to yield 40 candidate features. Fifteen machine learning methods were evaluated, and a ten-fold cross-validated elastic net regression model was selected to construct the radiomics-based prediction model. A clinical model based on five key clinical variables and a combined model integrating imaging and clinical features were also developed.</p><p><strong>Results: </strong>The radiomics model achieved accuracies of 0.801, 0.866, and 0.831 in the training set and two external test sets, with AUC of 0.791, 0.829, and 0.807. In one external test set, the clinical model had an AUC of 0.689, significantly lower than the radiomics model (0.807, p < 0.05). The combined model achieved the highest performance, with AUC of 0.834 in the training set and 0.894 in an external test set (p < 0.01 and p < 0.001, respectively). Interpretability analysis revealed that wavelet-transformed features dominated the model, with the highest contribution from a feature reflecting small high-intensity clusters within the tumor and the second highest from a feature representing low-intensity clusters in the six-voxel peritumoral region. Kaplan-Meier analysis demonstrated that patients with either pathologically confirmed or model-predicted spread had significantly shorter progression-free survival (p < 0.001).</p><p><strong>Conclusion: </strong>Our novel machine learning model, integrating imaging features from both tumor and peritumoral regions, preoperatively predicts tumor spread through air spaces in stage I lung adenocarcinoma. It outperforms traditional clinical models, highlighting the potential of quantitative imaging analysis in personalizing treatment. Future prospective studies and further optimization are warranted.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"115"},"PeriodicalIF":3.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiating pancreatic from periampullary non-pancreatic cancer: a nomogram-based prediction model utilizing CT imaging.","authors":"Xiaohuan Zhang, Junqing Wang, Wenjuan Wu, Zhuiyang Zhang, Fangming Chen, Lei Zhang","doi":"10.1186/s40644-025-00917-6","DOIUrl":"10.1186/s40644-025-00917-6","url":null,"abstract":"<p><strong>Background: </strong>To develop a predictive nomogram for differentiating pancreatic cancer from periampullary non-pancreatic cancers based on computed tomography (CT) imaging features.</p><p><strong>Methods: </strong>This retrospective study included 171 patients diagnosed with periampullary carcinoma (90 pancreatic cancer and 81 non-pancreatic cancer). Variables assessed included CT imaging features along with relevant clinical data. Statistically significant variables were identified through multivariable logistic regression analysis, and a predictive nomogram was developed and internally validated based on these factors.</p><p><strong>Results: </strong>Multivariable analysis identified the following independent risk factors: the distance from the distal end of the dilated pancreatic duct to the medial wall of the papilla (DPDP) (odds ratio [OR] 8.76, P < 0.05), the distance from the distal end of the dilated bile duct to the medial wall of the papilla (DBDP) (OR 31.83, P < 0.05), papillary enlargement (OR 0.03, P < 0.05), and visibility of pancreatic and/or bile ducts between the tumor and the papilla (VPBD) (OR 3.97, P < 0.05). A nomogram was constructed based on these four significant features. In both the development and validation cohorts, the nomogram demonstrated robust predictive performance, with areas under the receiver operating characteristic curve (AUCs) of 0.84 (95% CI, 0.77-0.91) and 0.81 (95% CI, 0.67-0.96), respectively.</p><p><strong>Conclusions: </strong>This study underscores the value of CT imaging features in distinguishing pancreatic cancer from periampullary non-pancreatic cancers. The identification of key imaging markers with significant diagnostic value facilitated the development and validation of a nomogram that integrates these features, providing a more reliable tool for clinical decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"114"},"PeriodicalIF":3.5,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.","authors":"Qing Peng, Ziyao Ji, Nan Xu, Zixian Dong, Tian Zhang, Mufei Ding, Le Qu, Yimo Liu, Jun Xie, Feng Jin, Bo Chen, Jiangdian Song, Ang Zheng","doi":"10.1186/s40644-025-00934-5","DOIUrl":"10.1186/s40644-025-00934-5","url":null,"abstract":"<p><strong>Background: </strong>Neoadjuvant chemotherapy (NAC) is a crucial therapeutic approach for treating breast cancer, yet accurately predicting treatment response remains a significant clinical challenge. Conventional ultrasound plays a vital role in assessing tumor morphology but lacks the ability to quantitatively capture intratumoral heterogeneity. Ultrasound radiomics, which extracts high-throughput quantitative imaging features, offers a novel approach to enhance NAC response prediction. This study aims to evaluate the predictive efficacy of ultrasound radiomics models based on pre-treatment, post-treatment, and combined imaging features for assessing the NAC response in patients with HER2-low breast cancer.</p><p><strong>Methods: </strong>This retrospective multicenter study included 359 patients with HER2-low breast cancer who underwent NAC between January 1, 2016, and December 31, 2020. A total of 488 radiomic features were extracted from pre- and post-treatment ultrasound images. Feature selection was conducted in two stages: first, Pearson correlation analysis (threshold: 0.65) was applied to remove highly correlated features and reduce redundancy; then, Recursive Feature Elimination with Cross-Validation (RFECV) was employed to identify the optimal feature subset for model construction. The dataset was divided into a training set (244 patients) and an external validation set (115 patients from independent centers). Model performance was assessed via the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score.</p><p><strong>Results: </strong>Three models were initially developed: (1) a pre-treatment model (AUC = 0.716), (2) a post-treatment model (AUC = 0.772), and (3) a combined pre- and post-treatment model (AUC = 0.762).To enhance feature selection, Recursive Feature Elimination with Cross-Validation was applied, resulting in optimized models with reduced feature sets: (1) the pre-treatment model (AUC = 0.746), (2) the post-treatment model (AUC = 0.712), and (3) the combined model (AUC = 0.759).</p><p><strong>Conclusions: </strong>Ultrasound radiomics is a non-invasive and promising approach for predicting response to neoadjuvant chemotherapy in HER2-low breast cancer. The pre-treatment model yielded reliable performance after feature selection. While the combined model did not substantially enhance predictive accuracy, its stable performance suggests that longitudinal ultrasound imaging may help capture treatment-induced phenotypic changes. These findings offer preliminary support for individualized therapeutic decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"112"},"PeriodicalIF":3.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of simultaneous multislice diffusion-weighted imaging using monoexponential, intravoxel incoherent motion, and diffusion kurtosis models: assessment of microvascular invasion and histologic grade in hepatocellular carcinoma.","authors":"Yingyi Wu, Zheng Qu, Ting Yang, Shan Yao, Jie Chen, Xinye Bao, Ting Yin, Bin Song, Zheng Ye","doi":"10.1186/s40644-025-00930-9","DOIUrl":"10.1186/s40644-025-00930-9","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to evaluate the diagnostic performance of simultaneous multislice (SMS) acquisition combined with monoexponential, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) models for predicting microvascular invasion (MVI) and histologic grade in hepatocellular carcinoma (HCC).</p><p><strong>Materials and methods: </strong>A prospective study was conducted with 77 HCC patients. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed on a 3T MRI using both SMS and conventional sequences. The values of diffusion parameters (ADC, D, D*, f, MD, and MK) were compared among SMS and conventional sequences, between MVI-positive and MVI-negative groups, and between high-grade and low-grade HCC groups. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of diffusion parameters in predicting MVI and histologic grade. Inter-reader consistency was evaluated using intraclass correlation coefficients (ICC).</p><p><strong>Results: </strong>Among the 77 patients, 29.9% were MVI-positive and 35.1% had high-grade HCC. SMS reduced scanning time by up to 44.44%. Most diffusion parameters were similar between SMS and conventional sequences, except for slightly lower ADC and f in SMS. MVI-positive and high-grade HCC cases showed lower ADC, D, D*, and MD values and higher MK values. The ICC ranged from 0.702 to 0.879. SMS-MK demonstrated the highest diagnostic performance with an AUC of 0.92 for MVI and 0.86 for histologic grade.</p><p><strong>Conclusions: </strong>SMS acquisition, integrated with IVIM and DKI, is a feasible imaging method for preoperative evaluation of MVI and histologic grade in HCC, offering a faster alternative to conventional methods without compromising diagnostic performance.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"113"},"PeriodicalIF":3.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value of early metabolic response for predicting axillary pathological complete response during neoadjuvant systemic therapy in triple negative and HER2-amplified breast cancers: impact of tumor subtypes.","authors":"Loup Guichard, Prescillia Nunes, Clémentine Jankowski, Aurélie Bertaut, Eloïse Michel, Sylvain Ladoire, Charles Coutant, Alexandre Cochet, Jean-Louis Alberini","doi":"10.1186/s40644-025-00914-9","DOIUrl":"10.1186/s40644-025-00914-9","url":null,"abstract":"<p><strong>Background: </strong>In the era of therapeutic de-escalation, the opportunity to move from systematic axillary lymph node dissection (ALND) to sentinel lymph node biopsy in axillary node-positive breast cancer patients after neoadjuvant systemic therapy (NST) is currently considered. The purpose of this study was to identify FDG-PET parameters associated with axillary pathological complete response (pRAx) in the most proliferative tumor subtypes, eg Triple Negative (TN) and HER2-amplified.</p><p><strong>Methods: </strong>Patients with newly-diagnosed TN or HER2-amplified breast cancer, with pathologically-proven axillary node metastasis, no distant metastasis and indication of NST were prospectively included from September 2017 to December 2021. Sequential FDG-PET/CT scans were performed at baseline and after one cycle of NST. Metabolic parameters at baseline and their changes (Delta in %) of axillary nodes were assessed: SUVmax, SUVratio (SUVmax/SUVmax liver), SUVpeak, SUVmean, TLG and MTV. Logistic regressions with ROC curves were used to determine parameters associated with pRAx.</p><p><strong>Results: </strong>Sixty-one patients (24 TN, 19 ER-negative/HER2-amplified and 18 ER-positive/HER2-amplified) were recruited. Median value of Axillary SUVmax at baseline were 7.9, 7.0 and 5.2 and Delta Axillary SUVmax were -62%,-60% and -47% in these 3 subgroups, respectively. In univariate model, in the whole population, Delta Axillary SUVmax showed the greatest AUC for prediction of pRAx of 0.72 (95%CI: 0.59-0.85), whereas AUC of Axillary SUVmax at baseline was not statistically significant (AUC = 0.6 (95%CI: 0.46-0.74)). Specificity, sensitivity, PPV and NPV of Delta Axillary SUVmax were 96%, 49%, 94% and 58% respectively for predicting pRAx with a threshold of -68.7%. Odd Ratio associated with Delta Axillary SUVmax < -68.7% compared to ≥ -68.7% was 24.0 (95%CI: 2.9-194). In multivariate model, adjusted on tumor subtypes, Delta Axillary SUVmax was still significantly associated with pRAx (OR = 20.7 (95%IC: 2.5-172). AUCs adjusted on the tumor subtype were not significantly modified compared to univariate model (p = 0.45 compared to unadjusted AUC) suggesting that thresholds were not significantly different in each tumor subtype.</p><p><strong>Conclusions: </strong>Delta Axillary SUVmax seems to be the most relevant metabolic parameter to predict an axillary pathological complete response and early metabolic response could be a valuable tool for selecting patients eligible for axillary surgical de-escalation after NST, regardless tumor subtypes.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"110"},"PeriodicalIF":3.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion for adult diffuse glioma genotyping.","authors":"Xiefeng Yang, Yu Lin, Yan Su, Lan Yu, Feng Wang, Xingfu Wang, Zhen Xing, Dairong Cao","doi":"10.1186/s40644-025-00931-8","DOIUrl":"10.1186/s40644-025-00931-8","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the effectiveness of histogram features from conventional diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) parameters in predicting the status of glioma IDH mutation and 1p/19q codeletion based on the 2021 WHO classification of central nervous system tumors.</p><p><strong>Methods and materials: </strong>A total of 422 participants who had DWI, DKI, and IVIM were enrolled between January 2020 and March 2024. The histogram characteristics of ADC, diffusional kurtosis(K), diffusion coefficient (Dk), pseudo-diffusion coefficient(D*), pure diffusion coefficient(D), perfusion fraction(f) in the solid component of tumors were calculated. Groups were compared by IDH genotype and 1p/19q codeletion status, utilizing logistic regression analysis and receiver operating characteristic curve to evaluate the differential diagnostic performance in predicting IDH and 1p/19q genotypes.</p><p><strong>Results: </strong>Significant differences were observed in thirty-nine histogram-based features of diffusion parameters between IDH mutant gliomas and IDH wildtype glioblastoma. In IDH mutant gliomas, significant differences were found in thirty-six histogram-based features of DWI, DKI and IVIM parameters between those with and without 1p/19q codeletion. The IVIM model and the combined model showed superior diagnostic performance compared to the DWI model in terms of AUCs for predicting IDH mutations (0.903, 0.913 and 0.807, respectively p < 0.05), and 1p/19q codeletion in IDH mutant gliomas (0.825, 0.855, and 0.769, respectively; p < 0.05). Correlations between Ki-67 and the mean values of ADC, Dk, K, D, D*, and f were significant, with correlation coefficients from - 0.17 to 0.36 (all p < 0.05).</p><p><strong>Conclusion: </strong>The prediction of IDH mutation status in adult diffuse glioma and the 1p/19q codeletion status in IDH mutant glioma could be improved through histogram features of IVIM-derived parameters and the combined model.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"111"},"PeriodicalIF":3.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the 24th International Cancer Imaging Society Meeting and Annual Teaching Course.","authors":"","doi":"10.1186/s40644-025-00919-4","DOIUrl":"10.1186/s40644-025-00919-4","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 Suppl 1","pages":"109"},"PeriodicalIF":3.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145022911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy.","authors":"Shaojie Xu, Yushi Ying, Qilan Hu, Xingyin Li, Yulin Li, Hao Xiong, Yanyan Chen, Qing Ye, Xingrui Li, Yue Liu, Tao Ai, Yaying Du","doi":"10.1186/s40644-025-00929-2","DOIUrl":"https://doi.org/10.1186/s40644-025-00929-2","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop a predictive model integrating multi-sequence MRI radiomics, deep learning features, and habitat imaging to forecast pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant therapy (NAT).</p><p><strong>Methods: </strong>A retrospective analysis included 203 breast cancer patients treated with NAT from May 2018 to January 2023. Patients were divided into training (n = 162) and test (n = 41) sets. Radiomics features were extracted from intratumoral and peritumoral regions in multi-sequence MRI (T2WI, DWI, and DCE-MRI) datasets. Habitat imaging was employed to analyze tumor subregions, characterizing heterogeneity within the tumor. We constructed and validated machine learning models, including a fusion model integrating all features, using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, decision curve analysis (DCA), and confusion matrices. Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses were performed for model interpretability.</p><p><strong>Results: </strong>The fusion model achieved superior predictive performance compared to single-region models, with AUCs of 0.913 (95% CI: 0.770-1.000) in the test set. PR curve analysis showed improved precision-recall balance, while DCA indicated higher clinical benefit. Confusion matrix analysis confirmed the model's classification accuracy. SHAP revealed DCE_LLL_DependenceUniformity as the most critical feature for predicting pCR and PC72 for non-pCR. LIME provided patient-specific insights into feature contributions.</p><p><strong>Conclusion: </strong>Integrating multi-dimensional MRI features with habitat imaging enhances pCR prediction in breast cancer. The fusion model offers a robust, non-invasive tool for guiding individualized treatment strategies while providing transparent interpretability through SHAP and LIME analyses.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"108"},"PeriodicalIF":3.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}